Wednesday, July 20, 2016

Cognitive Business: When Cloud and Cognitive Computing Merge

Cloud computing has
taken over the business world! With almost maniacal focus, single
proprietors and Board Directors of the world’s largest conglomerates see this
new model as a “must do”. This rapid shift is, in fact, accelerating. As Jeff
Bertolucci observes in “The
Shift to Cloud Services Is Happening Faster Than Expected”:

“According to the sixth annual Uptime Institute Data Center Industry Survey, which
examines the big-picture trends shaping IT infrastructure delivery and
strategy, the move to cloud services is accelerating. The Uptime Institute’s
February 2016 poll of more than 1,000 data center and IT professionals predicts
that an even faster shift to the cloud will occur over the next four years,
reports ZDNet.”

Another maybe even
more important trend, that is actually being driven by cloud computing, is the
rapid expansion of cognitive
computing. In this arena, IBM’s Watson, famously known for defeating
Jeopardy gameshow champions Ken Jennings and Brad Rutter,
has quickly established itself as a commercial cognitive computing powerhouse. Contemporary
reports of the Jeopardy contest from the New York Times cited this victory as
IBM’s “…proof that the company has taken a big step toward a world in which
intelligent machines will understand and respond to humans, and perhaps
inevitably, replace some of them”. Although we are not yet at the human
replacement stage, the merger of cloud and cognitive computing is rocking the
business status quo.

Coined as “Cognitive Business” this trend can deliver
quantum level improvement to just about any industry vertical. Examples
include:

Using highly automated and economic cloud
infrastructure to deliver proactive and predictive monitoring and threat
interception in cybersecurity;

Establishing connectivity across over 6.4
billion sensors so that analytics and cognitive computing programs can provide
actionable insight from real-time and historic data; and

Hybrid Cloud data architectures that use
cognitive computing capabilities to maintain content traceability and lifecycle
management to enable the auditable management of licenses, terms of use, and
changes to third-party data.

Cognitive systems understand by interaction, reason by
generating recommendations and hypotheses, and learn from human experts and data.
Since they never stop learning they also never stop providing business
value.With this blending of cloud
infrastructure and cognitive applications, the impossible can suddenly become
easy!

If your business wants to take advantage of this important
transition, the time to take action is now. Your initial steps should include:

Develop a cognitive strategy by deciding which of
your products, services, processes and operations should be infused with
cognition. Your strategy should include identifying your organizations data
needs and picking the experts to train the cognitive system.

Collect and curate the data that is most useful
to driving your business model.This
step will help in creating an organizational foundation of data and analytics.

Use cloud services that are designed specifically
for your industry vertical. Such services will incorporate the application
programming interface (API) building blocks necessary to power your future
cognitive products and services.

Acquire hybrid cloud service broker expertise
and develop a hybrid infrastructure transition plan that combines your current
IT systems with private and/or public clouds. This combination will serve as a
backbone of your cognitive business.

Establish and build-in a data-centric security
model from the start. This focus will give you the ability to secure every
transaction, piece of data and interaction as cognitive systems make their way
into the Internet-of-Things (IoT). Secure systems ensure trust in the entire
system and ultimately, the organization’s reputation.

Transform
from the delivery of labor hours and physical goods to the delivery of
information and services (Uber, Air B&B, Travelocity, etc)

In the case
of IBM Watson, the consumption and processing of billions of API calls per
month across 80,000 programs developed by 500 companies in 36 countries
(http://www.tomsitpro.com/articles/ibm-cloud-hyrid-storage-watson,1-3158.html)

Consulting /
Analytics

Voice-driven
command and control

Applications
that understand natural language and generate personalized insights that
learn with every user interaction

Consulting

Economic and
secure collection, transport, processing and storage of massive amounts of
structured and unstructured data

Ability to pull non-obvious insights out of
massive amounts of multi-structured data through the discovery of patterns
and relationships. This enables the economic use of dark data, described as
"information assets that organizations collect, process and store in the
course of their regular business activity, but generally fail to use for
other purposes."

Distribution
/ Publishing / Content Management

Hybrid Cloud
data architectures that ensures that, when multiple data zones are in use,
compute power is moved to the data, rather than compute workloads being moved
in a way that could violate institutional policies, regulatory guidelines or
governmental laws around data location.

Data Fabric
technology that maintains the traceability and lifecycle of content enabling
the auditable management of licenses, terms of use, and changes to
third-party data.

Education /
Research

Global
SaaS/PaaS business models and platforms

Creation of
Analytic Fabrics that combine and orchestrate different analytics engines
that deliver an ability to create composite or cognitive insights across
first-part and third-party data. This can also be used to combine natural
language queries with structured data analytics.

Healthcare /
Public Safety

Global
SaaS/PaaS business models and platform

Cognitive Graphs that can represent entities, relationships, and
attributes in a probabilistic way, not just a deterministic way, so that
users can do inferencing and generate hypothesis. This also delivers an
ability to normalize many different data types as well as learn from data
over time. With this capability, when something changes somewhere in the
graph that may affect something elsewhere in the graph, every specific change
is recognized at every point it touches.